DrugUtilisation

Conduct Drug Utilisation Studies in OMOP CDM

2025-06-26

Introduction

Drug utilisation studies (DUS) were defined by the World Health Organization as studying the marketing, distribution, prescription, and use of medicinal products in a society, with special emphasis on the resulting medical and socioeconomic consequences (WHO, 2003).

This package aims to standardise and provide the tools to conduct Drug Utilisation studies as of the Darwin-EU Catalogue of Standard Analytics.

The package


install.packages("DrugUtilisation")


Create a reference to a cdm object

For this example we are going to use a synthetic test database:

library(omock)
library(duckdb)
library(CDMConnector)
library(dplyr)
library(CodelistGenerator)
library(DrugUtilisation)
library(CohortConstructor)

cdm <- mockCdmFromDataset(datasetName = "synthea-covid19-10k")
cdm$drug_exposure <- cdm$drug_exposure |>
  mutate(quantity = sample(c(0, 1, 5, 10, 30, 100), size = dplyr::n(), replace = TRUE))
con <- dbConnect(drv = duckdb())
src <- dbSource(con = con, writeSchema = "main")
cdm <- insertCdmTo(cdm = cdm, to = src)

Generate a drug cohort

For this example we are going to use all records of acteaminophen:

codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen", nameStyle = "{concept_name}")
cdm$dus_cohort <- conceptCohort(
  cdm = cdm,
  name = "dus_cohort",
  conceptSet = codelist
)

The results is the cdm object with the new cohort instantiated.

cdm
── # OMOP CDM reference (duckdb) of synthea-covid19-10k ────────────────────────────────────────────────────────────────
• omop tables: person, observation_period, visit_occurrence, visit_detail, condition_occurrence, drug_exposure,
procedure_occurrence, device_exposure, measurement, observation, death, note, note_nlp, specimen, fact_relationship,
location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, condition_era, metadata, cdm_source,
concept, vocabulary, domain, concept_class, concept_relationship, relationship, concept_synonym, concept_ancestor,
source_to_concept_map, drug_strength, cohort_definition, attribute_definition
• cohort tables: dus_cohort
• achilles tables: -
• other tables: cohort_count, cohort_set, cohort

Cohort objects

The cohort objects have some attributes:

class(cdm$dus_cohort)
[1] "cohort_table"          "GeneratedCohortSet"    "cdm_table"             "tbl_duckdb_connection"
[5] "tbl_dbi"               "tbl_sql"               "tbl_lazy"              "tbl"                  
names(attributes(cdm$dus_cohort))
[1] "names"            "class"            "tbl_source"       "tbl_name"         "cohort_set"       "cohort_attrition"
[7] "cohort_codelist"  "cdm_reference"
  • Settings
  • Counts
  • Attrition
  • Codelist

Cohort attributes

Settings:

settings(cdm$dus_cohort)
# A tibble: 1 × 4
  cohort_definition_id cohort_name   cdm_version vocabulary_version
                 <int> <chr>         <chr>       <chr>             
1                    1 acetaminophen 5.3         v5.0 22-JUN-22

Cohort counts:

cohortCount(cdm$dus_cohort)
# A tibble: 1 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1           2600             595

Cohort attributes

Cohort attrition:

attrition(cdm$dus_cohort)
# A tibble: 6 × 7
  cohort_definition_id number_records number_subjects reason_id reason                excluded_records excluded_subjects
                 <int>          <int>           <int>     <int> <chr>                            <int>             <int>
1                    1           2645             595         1 Initial qualifying e…                0                 0
2                    1           2645             595         2 Record start <= reco…                0                 0
3                    1           2645             595         3 Record in observation                0                 0
4                    1           2645             595         4 Non-missing sex                      0                 0
5                    1           2645             595         5 Non-missing year of …                0                 0
6                    1           2600             595         6 Merge overlapping re…               45                 0

Cohort codelist:

cohortCodelist(cdm$dus_cohort, cohortId = 1)

- acetaminophen (25747 codes)

gapEra parameter

gapEra parameter can be used to concatenate different records.

Number of days between two continuous exposures to be considered in the same era. Records that have fewer days between them than this gap will be concatenated into the same cohort record.

gapEra parameter

Generate a drug cohort

cdm$dus_cohort <- cdm$dus_cohort |>
  collapseCohorts(gap = 30)

Let’s check the settings again:

settings(cdm$dus_cohort)
# A tibble: 1 × 4
  cohort_definition_id cohort_name   cdm_version vocabulary_version
                 <int> <chr>         <chr>       <chr>             
1                    1 acetaminophen 5.3         v5.0 22-JUN-22

Inclusion criteria

Four functions to add inclusion criteria to our cohort:

Important considerations

Not all inclusion criteria are commutable operations (A + B != B + A):

Not commutable:

  • prior washout + prior observation != prior observation + prior washout
  • prior washout + first != first + prior washout
  • prior washout + date range != date range + prior washout
  • first + prior observation != prior observation + first
  • first + date range != date range + first

Commutable:

  • date range + prior observation == prior observation + date range

Vignette to create cohorts

https://darwin-eu-dev.github.io/DrugUtilisation/articles/create_cohorts.html

Your turn

Can you create a cohort with all the records of ‘simvastatin’ concatenating records separated by 90 days or less and the following inclusion criteria:

  • First record ever

  • At least 365 days of prior observation

  • Cohort start date between ‘2010-01-01’ and ‘2021-12-31’.

# A tibble: 1 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1            366             366
💡 Click to see solution 
codelist <- getDrugIngredientCodes(cdm = cdm, name = "simvastatin")
cdm$simvastatin_cohort <- conceptCohort(
  cdm = cdm, 
  conceptSet = codelist, 
  name = "simvastatin_cohort"
) |>
  collapseCohorts(gap = 90) |>
  requireIsFirstEntry() |>
  requirePriorObservation(minPriorObservation = 365) |>
  requireInDateRange(dateRange = as.Date(c('2010-01-01', '2021-12-31')))
cohortCount(cdm$simvastatin_cohort)
# A tibble: 1 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1            366             366

Drug usage

result <- cdm$dus_cohort |>
  summariseDrugUtilisation(
    ingredientConceptId = 1361711, 
    conceptSet = codelist,
    indexDate = "cohort_start_date",
    censorDate = "cohort_end_date", 
    restrictIncident = TRUE, 
    gapEra = 30, 
    numberExposures = TRUE, 
    numberEras = TRUE,
    daysExposed = TRUE,
    daysPrescribed = TRUE,
    timeToExposure = FALSE, 
    initialQuantity = TRUE, 
    cumulativeQuantity = TRUE, 
    initialDailyDose = TRUE, 
    cumulativeDose = TRUE,
    estimates = c("q25", "median", "q75")
  )

Drug usage

result |>
  suppress(minCellCount = 5) |>
  tableDrugUtilisation()
Concept set Variable name Estimate name
CDM name
synthea-covid19-10k
acetaminophen
overall number records N 609
number subjects N 595
36567_simvastatin number exposures Median (Q25 - Q75) 0 (0 - 0)
cumulative quantity Median (Q25 - Q75) 0.00 (0.00 - 0.00)
initial quantity Median (Q25 - Q75) 0.00 (0.00 - 0.00)
initial exposure duration Median (Q25 - Q75) 1 (1 - 1)
number eras Median (Q25 - Q75) 0 (0 - 0)
days exposed Median (Q25 - Q75) 0 (0 - 0)
days prescribed Median (Q25 - Q75) 0 (0 - 0)

Indications

summariseIndication():

  • To summarise mutually exclusive indications.

  • Define a window respect to the ‘cohort_start_date’.

  • Indications must be instantiated beforehand as cohorts.

  • Unknown indication (check a table to see if there is a record).

Indications

Let’s instantiate the cohorts of interest:

cdm$indications <- conceptCohort(
  cdm = cdm,
  conceptSet = list(
    arteriosclerosis = 317576, 
    myocardial_infarction = 4329847, 
    pain = getCandidateCodes(cdm = cdm, keywords = "pain")$concept_id
  ),
  name = "indications"
)

Indciations

result <- cdm$dus_cohort |>
  summariseIndication(
    indicationCohortName = "indications", 
    indicationWindow = list(c(0, 0), c(-30, 7)), 
    unknownIndicationTable = "condition_occurrence"
  )
result |>
  glimpse()
Rows: 40
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "synthea-covid19-10k", "synthea-covid19-10k", "synthea-covid19-10k", "synthea-covid19-10k", "…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "acetaminophen", "acetaminophen", "acetaminophen", "acetaminophen", "acetaminophen", "acetami…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Indication on index date", "Indication on index date", "Indication on index date", "Indicati…
$ variable_level   <chr> "arteriosclerosis", "arteriosclerosis", "myocardial_infarction", "myocardial_infarction", "pa…
$ estimate_name    <chr> "count", "percentage", "count", "percentage", "count", "percentage", "count", "percentage", "…
$ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "integer", "percentage", "integer", "percen…
$ estimate_value   <chr> "0", "0", "408", "66.9950738916256", "60", "9.85221674876847", "2", "0.328407224958949", "0",…
$ additional_name  <chr> "window_name", "window_name", "window_name", "window_name", "window_name", "window_name", "wi…
$ additional_level <chr> "0 to 0", "0 to 0", "0 to 0", "0 to 0", "0 to 0", "0 to 0", "0 to 0", "0 to 0", "0 to 0", "0 …

Indications

result |>
  suppress() |>
  tableIndication()
CDM name
synthea-covid19-10k
Indication Estimate name
Cohort name
acetaminophen
Indication on index date
arteriosclerosis N (%) 0 (0.00 %)
myocardial_infarction N (%) 408 (67.00 %)
pain N (%) 60 (9.85 %)
arteriosclerosis and myocardial_infarction N (%) <5
arteriosclerosis and pain N (%) 0 (0.00 %)
myocardial_infarction and pain N (%) 0 (0.00 %)
arteriosclerosis and myocardial_infarction and pain N (%) 0 (0.00 %)
unknown N (%) 104 (17.08 %)
none N (%) 35 (5.75 %)
not in observation N (%) 0 (0.00 %)
Indication from 30 days before to 7 days after the index date
arteriosclerosis N (%) 0 (0.00 %)
myocardial_infarction N (%) 413 (67.82 %)
pain N (%) 63 (10.34 %)
arteriosclerosis and myocardial_infarction N (%) <5
arteriosclerosis and pain N (%) 0 (0.00 %)
myocardial_infarction and pain N (%) 0 (0.00 %)
arteriosclerosis and myocardial_infarction and pain N (%) 0 (0.00 %)
unknown N (%) 101 (16.58 %)
none N (%) 30 (4.93 %)
not in observation N (%) 0 (0.00 %)

Indications

Indications

Output of ploting functions are ggplot2 objects.

plotIndication(result) +
  ggplot2::theme(legend.position = "none")

Indications

Currently considering to add support for plotly.

plotIndication(result) |>
  plotly::ggplotly()

Treatment persistence

To analyse treatment persistence and adherence there are two standard pipelines:

  • Survival analysis (implemented in the CohortSurvival package)

  • Proportion of patients covered

Proportion of patients covered

Proportion of treated individuals between index date and followUpDays. Percentages will be reported daily.

result <- cdm$dus_cohort |>
  summariseProportionOfPatientsCovered(followUpDays = 90)

Proportion of patients covered

Time Estimate name
Cohort name
acetaminophen
synthea-covid19-10k
0 PPC (95%CI) 100.00% [99.36% - 100.00%]
1 PPC (95%CI) 27.61% [24.17% - 31.34%]
2 PPC (95%CI) 27.61% [24.17% - 31.34%]
3 PPC (95%CI) 27.61% [24.17% - 31.34%]
4 PPC (95%CI) 27.61% [24.17% - 31.34%]
5 PPC (95%CI) 27.61% [24.17% - 31.34%]
6 PPC (95%CI) 26.94% [23.53% - 30.64%]
7 PPC (95%CI) 26.60% [23.20% - 30.29%]
8 PPC (95%CI) 74.76% [68.48% - 80.16%]
9 PPC (95%CI) 74.64% [68.33% - 80.06%]
10 PPC (95%CI) 66.83% [60.17% - 72.87%]
11 PPC (95%CI) 53.85% [47.06% - 60.49%]
12 PPC (95%CI) 44.23% [37.65% - 51.02%]
13 PPC (95%CI) 31.88% [25.92% - 38.51%]
14 PPC (95%CI) 22.33% [17.18% - 28.49%]
15 PPC (95%CI) 14.15% [10.03% - 19.58%]
16 PPC (95%CI) 11.27% [7.63% - 16.35%]
17 PPC (95%CI) 10.34% [6.87% - 15.30%]
18 PPC (95%CI) 6.44% [3.80% - 10.70%]
19 PPC (95%CI) 2.49% [1.07% - 5.69%]
20 PPC (95%CI) 2.54% [1.09% - 5.80%]
21 PPC (95%CI) 1.02% [0.28% - 3.64%]
22 PPC (95%CI) 0.51% [0.09% - 2.85%]
23 PPC (95%CI) 0.00% [0.00% - 1.94%]
24 PPC (95%CI) 0.00% [0.00% - 1.97%]
25 PPC (95%CI) 0.00% [0.00% - 2.01%]
26 PPC (95%CI) 0.00% [0.00% - 2.06%]
27 PPC (95%CI) 0.00% [0.00% - 2.09%]
28 PPC (95%CI) 0.00% [0.00% - 2.12%]
29 PPC (95%CI) 0.00% [0.00% - 2.17%]
30 PPC (95%CI) 0.00% [0.00% - 2.24%]
31 PPC (95%CI) 0.00% [0.00% - 2.25%]
32 PPC (95%CI) 0.00% [0.00% - 2.30%]
33 PPC (95%CI) 0.00% [0.00% - 2.32%]
34 PPC (95%CI) 0.00% [0.00% - 2.33%]
35 PPC (95%CI) 0.00% [0.00% - 2.33%]
36 PPC (95%CI) 0.00% [0.00% - 2.36%]
37 PPC (95%CI) 0.00% [0.00% - 2.36%]
38 PPC (95%CI) 0.00% [0.00% - 2.36%]
39 PPC (95%CI) 0.00% [0.00% - 2.36%]
40 PPC (95%CI) 0.00% [0.00% - 2.36%]
41 PPC (95%CI) 0.00% [0.00% - 2.36%]
42 PPC (95%CI) 0.00% [0.00% - 2.36%]
43 PPC (95%CI) 0.00% [0.00% - 2.37%]
44 PPC (95%CI) 0.00% [0.00% - 2.37%]
45 PPC (95%CI) 0.00% [0.00% - 2.37%]
46 PPC (95%CI) 0.00% [0.00% - 2.37%]
47 PPC (95%CI) 0.00% [0.00% - 2.37%]
48 PPC (95%CI) 0.00% [0.00% - 2.37%]
49 PPC (95%CI) 0.00% [0.00% - 2.37%]
50 PPC (95%CI) 0.00% [0.00% - 2.37%]
51 PPC (95%CI) 0.00% [0.00% - 2.37%]
52 PPC (95%CI) 0.00% [0.00% - 2.37%]
53 PPC (95%CI) 0.00% [0.00% - 2.37%]
54 PPC (95%CI) 0.00% [0.00% - 2.37%]
55 PPC (95%CI) 0.00% [0.00% - 2.37%]
56 PPC (95%CI) 0.00% [0.00% - 2.37%]
57 PPC (95%CI) 0.00% [0.00% - 2.39%]
58 PPC (95%CI) 0.00% [0.00% - 2.39%]
59 PPC (95%CI) 0.00% [0.00% - 2.39%]
60 PPC (95%CI) 0.00% [0.00% - 2.40%]
61 PPC (95%CI) 0.00% [0.00% - 2.40%]
62 PPC (95%CI) 0.00% [0.00% - 2.40%]
63 PPC (95%CI) 0.00% [0.00% - 2.40%]
64 PPC (95%CI) 0.00% [0.00% - 2.42%]
65 PPC (95%CI) 0.00% [0.00% - 2.42%]
66 PPC (95%CI) 0.00% [0.00% - 2.42%]
67 PPC (95%CI) 0.00% [0.00% - 2.42%]
68 PPC (95%CI) 0.00% [0.00% - 2.42%]
69 PPC (95%CI) 0.00% [0.00% - 2.42%]
70 PPC (95%CI) 0.00% [0.00% - 2.42%]
71 PPC (95%CI) 0.00% [0.00% - 2.42%]
72 PPC (95%CI) 0.00% [0.00% - 2.42%]
73 PPC (95%CI) 0.00% [0.00% - 2.42%]
74 PPC (95%CI) 0.00% [0.00% - 2.42%]
75 PPC (95%CI) 0.00% [0.00% - 2.42%]
76 PPC (95%CI) 0.00% [0.00% - 2.42%]
77 PPC (95%CI) 0.00% [0.00% - 2.42%]
78 PPC (95%CI) 0.00% [0.00% - 2.42%]
79 PPC (95%CI) 0.00% [0.00% - 2.42%]
80 PPC (95%CI) 0.00% [0.00% - 2.42%]
81 PPC (95%CI) 0.00% [0.00% - 2.43%]
82 PPC (95%CI) 0.00% [0.00% - 2.43%]
83 PPC (95%CI) 0.00% [0.00% - 2.43%]
84 PPC (95%CI) 0.00% [0.00% - 2.43%]
85 PPC (95%CI) 0.00% [0.00% - 2.43%]
86 PPC (95%CI) 0.00% [0.00% - 2.43%]
87 PPC (95%CI) 0.00% [0.00% - 2.43%]
88 PPC (95%CI) 0.00% [0.00% - 2.43%]
89 PPC (95%CI) 0.00% [0.00% - 2.43%]
90 PPC (95%CI) 0.00% [0.00% - 2.43%]

Proportion of patients covered

gapEra parameter can have a big impact in survival analysis, whereas it does not have such big impact on proportion of patients covered.

Your turn

Can you create a cohort of a certain ingredient and analyse its persistence in the following year?

suggestions: ‘clopidogrel’, ‘nitroglycerin’, ‘simvastatin’, ‘amlodipine’, ‘verapamil’ are the 5 most common ingredients in this database

Solution

💡 Click to see solution 
codelist <- getDrugIngredientCodes(
  cdm = cdm, 
  name = c('clopidogrel', 'nitroglycerin', 'simvastatin', 'amlodipine', 'verapamil')
)
cdm$persistence <- conceptCohort(
  cdm = cdm, 
  name = "persistence",
  conceptSet = codelist
)
result <- summariseProportionOfPatientsCovered(cohort = cdm$persistence, followUpDays = 365)
plotProportionOfPatientsCovered(result = result, colour = "cohort_name")

Drug restart and drug switching

We have the ability to study drug restart or drug switching after a discontinuation:

  • Switching cohorts must be defined in advance.

  • We have to define the windows of interest.

  • Index date will be the date of discontinuation.

  • Reported percentages in each window will be:

  • Restart: individuals that restarted and not switched.

  • Switch: individuals that switched and not restarted.

  • Restart and switch: individuals that restarted and switched.

  • Untreated: individuals that did not restart or switch.

Drug restart and drug switching

Let’s define the cohorts of interest for switching:

codelist <- getDrugIngredientCodes(
  cdm = cdm, 
  name = c("clopidogrel", "simvastatin", "warfarin")
)
cdm$switch <- conceptCohort(
  cdm = cdm, 
  name = "switch",
  conceptSet = codelist
)

Drug restart and drug switching

We have to be careful with the inclusion criteria of the input cohort, as restart is derived from it.

result <- cdm$dus_cohort |>
  summariseDrugRestart(
    switchCohortTable = "switch", 
    followUpDays = c(90, 180, 270, 360), 
    restrictToFirstDiscontinuation = TRUE
  )
result |>
  glimpse()
Rows: 32
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
$ cdm_name         <chr> "synthea-covid19-10k", "synthea-covid19-10k", "synthea-covid19-10k", "synthea-covid19-10k", "…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "acetaminophen", "acetaminophen", "acetaminophen", "acetaminophen", "acetaminophen", "acetami…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Drug restart in 90 days", "Drug restart in 90 days", "Drug restart in 90 days", "Drug restar…
$ variable_level   <chr> "restart", "restart", "switch", "switch", "restart and switch", "restart and switch", "untrea…
$ estimate_name    <chr> "count", "percentage", "count", "percentage", "count", "percentage", "count", "percentage", "…
$ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "integer", "percentage", "integer", "percen…
$ estimate_value   <chr> "0", "0", "43", "7.22689075630252", "0", "0", "552", "92.7731092436975", "0", "0", "51", "8.5…
$ additional_name  <chr> "follow_up_days", "follow_up_days", "follow_up_days", "follow_up_days", "follow_up_days", "fo…
$ additional_level <chr> "90 days", "90 days", "90 days", "90 days", "90 days", "90 days", "90 days", "90 days", "180 …

Drug restart and drug switching

CDM name
synthea-covid19-10k
Treatment Estimate name
Cohort name
acetaminophen
Drug restart in 90 days
restart N (%) 0 (0.00 %)
switch N (%) 43 (7.23 %)
restart and switch N (%) 0 (0.00 %)
untreated N (%) 552 (92.77 %)
Drug restart in 180 days
restart N (%) 0 (0.00 %)
switch N (%) 51 (8.57 %)
restart and switch N (%) 0 (0.00 %)
untreated N (%) 544 (91.43 %)
Drug restart in 270 days
restart N (%) 0 (0.00 %)
switch N (%) 59 (9.92 %)
restart and switch N (%) 1 (0.17 %)
untreated N (%) 535 (89.92 %)
Drug restart in 360 days
restart N (%) 0 (0.00 %)
switch N (%) 65 (10.92 %)
restart and switch N (%) 3 (0.50 %)
untreated N (%) 527 (88.57 %)

Drug restart and drug switching

Summarise treatments

summariseTreatment() is a general function to analyse presence of treatments (cohorts) after an index date. This can be used with many different purposes:

  • Summarise treatments after a certain condition

  • Analyse comedications

  • Analyse treatments after discontinuation

  • …

Summarise treatments

You have to instantiate the treatments that you are interested in a cohort:

codelist <- getDrugIngredientCodes(
  cdm = cdm, 
  name = c("clopidogrel", "simvastatin", "warfarin", "nitroglycerin")
)
cdm$treatments <- conceptCohort(
  cdm = cdm, 
  name = "treatments",
  conceptSet = codelist
)
result <- cdm$dus_cohort |>
  summariseTreatment(
    window = list(c(1, 90), c(91, 180), c(181, 270), c(271, 360)), 
    treatmentCohortName = "treatments", 
    indexDate = "cohort_end_date", 
    censorDate = NULL
  )
result |>
  glimpse()
Rows: 48
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "synthea-covid19-10k", "synthea-covid19-10k", "synthea-covid19-10k", "synthea-covid19-10k", "…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "acetaminophen", "acetaminophen", "acetaminophen", "acetaminophen", "acetaminophen", "acetami…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "Medication from 1 day after to 90 days after the index date", "Medication from 1 day after t…
$ variable_level   <chr> "11289_warfarin", "11289_warfarin", "32968_clopidogrel", "32968_clopidogrel", "36567_simvasta…
$ estimate_name    <chr> "count", "percentage", "count", "percentage", "count", "percentage", "count", "percentage", "…
$ estimate_type    <chr> "integer", "percentage", "integer", "percentage", "integer", "percentage", "integer", "percen…
$ estimate_value   <chr> "0", "0", "19", "3.11986863711002", "1", "0.164203612479475", "18", "2.95566502463054", "586"…
$ additional_name  <chr> "window_name", "window_name", "window_name", "window_name", "window_name", "window_name", "wi…
$ additional_level <chr> "1 to 90", "1 to 90", "1 to 90", "1 to 90", "1 to 90", "1 to 90", "1 to 90", "1 to 90", "1 to…

Summarise treatments

CDM name
synthea-covid19-10k
Treatment Estimate name
Cohort name
acetaminophen
Medication from 1 day after to 90 days after the index date
11289_warfarin N (%) 0 (0.00 %)
32968_clopidogrel N (%) 19 (3.12 %)
36567_simvastatin N (%) 1 (0.16 %)
4917_nitroglycerin N (%) 18 (2.96 %)
untreated N (%) 586 (96.22 %)
not in observation N (%) 4 (0.66 %)
Medication from 91 days after to 180 days after the index date
11289_warfarin N (%) 1 (0.16 %)
32968_clopidogrel N (%) 13 (2.13 %)
36567_simvastatin N (%) 0 (0.00 %)
4917_nitroglycerin N (%) 11 (1.81 %)
untreated N (%) 142 (23.32 %)
not in observation N (%) 452 (74.22 %)
Medication from 181 days after to 270 days after the index date
11289_warfarin N (%) 0 (0.00 %)
32968_clopidogrel N (%) 20 (3.28 %)
36567_simvastatin N (%) 1 (0.16 %)
4917_nitroglycerin N (%) 20 (3.28 %)
untreated N (%) 133 (21.84 %)
not in observation N (%) 454 (74.55 %)
Medication from 271 days after to 360 days after the index date
11289_warfarin N (%) 0 (0.00 %)
32968_clopidogrel N (%) 18 (2.96 %)
36567_simvastatin N (%) 0 (0.00 %)
4917_nitroglycerin N (%) 17 (2.79 %)
untreated N (%) 131 (21.51 %)
not in observation N (%) 458 (75.21 %)

Summarise treatments

DrugUtilisation

👉 Packages website
👉 CRAN link
👉 Manual

📧 marti.catalasabate@ndorms.ox.ac.uk